Generating Macro-operators by Exploiting Inner Entanglements

In Automated Planning, learning and exploiting additional
knowledge within a domain model, in order
to improve plan generation speed-up and increase
the scope of problems solved, has attracted much research.
Reformulation techniques such as those based
on macro-operators or entanglements are very promising
because they are to some extent domain model and
planning engine independent. This paper aims to exploit
recent work on inner entanglements, relations between
pairs of planning operators and predicates encapsulating
exclusivity of predicate ‘achievements‘ or ‘requirements’,
for generating macro-operators. We provide
a theoretical study resulting in a set of conditions
when planning operators in an inner entanglement relation
can be removed from a domain model and replaced
by a macro-operator without compromising solvability
of a given (class of) problem(s). The effectiveness of
our approach will be experimentally shown on a set
of well-known benchmark domains using several highperforming
planning engines.